 Thanks for that introduction. I'm very excited to be here from Uber's data visualization and urban computing team, talk about how we are using urban computing and how we're really embracing open source to do that. Let me start really briefly by giving an overview of what, at a high level, urban computing is. So from an urban aspect, urban computing is about tackling challenges that cities have across a broad area of safety, road infrastructure, equitable access to transportation, energy consumption, traffic congestion, tons of applications of how we see urban computing can be used. In terms of how we approach that, urban computing really starts from data across a wide variety of heterogeneous sources. So that can be data from sensors. It could be data that's collected from incidents or government agencies. We combine that data together. We use data science to analyze it, aggregate it, process it. We use data visualization techniques to provide insights into that data, again, so that we can tackle challenges that cities may have across a variety of different areas. So that's at a high level what urban computing is to help that sink in. I wanna just walk through a really brief example of how we're thinking about urban computing in the context of transportation. So for context, this graph right here shows over the last decade, traffic fatalities. We can see that it was declining since 2007 and then in 2015 and 16, there was an increase in traffic fatalities, about 5% year over year. And transportation decision makers, policy makers, they had some information about this, but they didn't have a lot of detailed granular insight into what was leading to this. Even more specifically, there was an incident on December 27th, 2017. Traffic fatality killed a 17 year old passenger in New York City at the corner of Canal and Bowery Street, this is a tragic incident. And I bring this up because from this specific example here, we can start to, using urban computing, glean some important insights and use those insights more broadly to understand potentially what's leading to these traffic increase in traffic fatalities. So the US Department of Transportation knows that of the 40,000 traffic fatalities a year, over 10,000 of those are caused by speeding and speeding is the primary factor in this. So this visualization up here is showing Manhattan, New York City, where Uber operates. And it's also illustrating how we're using urban computing to really provide insights into how people are moving about a city. So you can see that there's road segments colored here and scatter plots of data points. So this is bringing that heterogeneous collection of data from different sources and combining it together into a single visualization. The red dots here are traffic fatalities from the US Department of Transportation. There's yellow dots that are also bringing in data from the New York City Police Department. And then the blue road segments, blue, yellow and red is data that Uber has processed from GPS sensors, GPS signals from our drivers moving around the city, combined that together, processed it, anonymized it, aggregated it to be able to provide information about average vehicle speeds down to a high degree of granularity in both space and time. So this is zooming in on the site of that accident canal on Bowery Street, where we're seeing two sources of data here. And we see that certain road segments here are, we can see that it's data combined over down to road segment granularity and also hour by hour. And we can see that there's particular road segments here where we have significant speed above the speed limit, all hours of the day. We can start to use that information to see that there's other similar, provide insights into where there's other similar parts of the city that may be having, we're seeing the same type of vehicle speed behavior. Maybe they don't have the same type of traffic fatalities or the other sources of data there, but transportation decision makers and policy makers can really use this data to not only get retrospective insight, but also start to use the visualization tools, start to explore how they can use this to find other areas where they can impact meaningful change. So again, that's just one example of how we're exploring using data acquisition, data science, visualization, simulation, combining those together into this theme of urban computing. Uber's thinking about urban computing more than just safety, but also congestion, pollution, increasing and providing more equitable access to transportation. So I really wanna talk a little bit more about how we're doing that and how we're doing that with open source. So the previous visualizations were all created using Kepler GL for cities. So this is an open source web-based visualization tool that we've created at Uber. It's open, it's free, it's fast, it's easy to use, it's web-based, it actually doesn't require any programming to use, so it's the customers for our days as scientists, engineers, urban planners, researchers, civic hackers. Basically, you can drag and drop data into this web-based tool, free and open-source web-based tool, and powering it is high-performance, computer graphics-based WebGL code that's basically able to render millions of data points in a really high-performance way in the web browser. So that's Kepler GL for cities, and this is what the previous visualizations were created with. Underlying Kepler GL is a whole set of visualization frameworks that are powering it. So it's kind of the foundation of Uber's urban computing platform. These frameworks are suited for a variety of applications. They're largely web-based, and they're largely around just being able to display data, millions of data points really performantly in the web browser, and there's a lot of geospatial visualization use cases for this. But you can also see that they're powerful enough to be applied to some related areas that are potentially related to urban computing and transportation, like self-driving car visualization. We've just recently open-sourced our autonomous visualization system, which is a set of components and a visualization standard for autonomous vehicle systems. The other aspect of this I want to touch on is data, and how Uber is trying to provide anonymized data for over two billion trips to really help our urban planners around the world. So we're doing that through our investments in what we call the Uber movement platform. So this is an open platform where we're providing data for regulators, policymakers, researchers, civic hackers, and we're doing this in a way that is very privacy-minded. So we anonymize and aggregate that data in a way so that the regulators and policymakers can glean useful insights for it and use it for their urban computing needs. So that's a broad overview of some of the specific technologies and specific open-source software frameworks that we're building, software frameworks and tools. As we've built this technology, we're starting to see that we're getting like a groundswell or attention from like a community of developers who are noticing this and starting to use it in their applications. So we're seeing that through people actively contributing, participating in the conversation, contributing code on our open-source GitHub repos, which is really exciting to see. We're really starting to focus on fostering this and creating an uproar ecosystem around this through really improving our documentation, making these things more accessible and spreading awareness of them. We're also teaching courses in data visualization at universities and engaging with researchers and academic institutions, civic hackers. So that's where we are right now. I think we are, we can see a lot of potential in what urban computing is and we know that open-source is at the core of this. So we have partnerships with governments, external companies, researchers, the open-source community, but we really want to, we see the future, a lot of potential in terms of accelerating and expanding an ecosystem around these tools. So we're exploring creating like a neutral foundation to accelerate this using the opening governance models and support and guidance and help from the Linux Foundation. And the real goal of this is to create a framework for the long-term growth, sustainability, stewardship and technical improvement of these tools and frameworks for urban computing. And so we can't do it alone. So we're here to talk about this, start to spread awareness of this and also ask for interest about it. So if you're interested in helping with this, we can't do it alone. Please come and talk to us. You can drop us a line at the Linux Foundation slash project slash urban computing. And I think that there's a lot of potential to really help cities and citizens improve the lives of everybody in our cities through really building this sustainable growing ecosystem around urban computing in the future. Thank you very much. All right. Thank you. We'll get to it, pull on one second. You know, one of the things I love about this project is you've got an open source set of projects, but you also have a data sharing project which I think is really, really interesting. And I think we're gonna see a lot more of this in the future, but you're like an early sort of vanguard for taking data, anonymizing it, sharing it. So it's sort of open source, but applied to data. So awesome stuff. We look forward to working with you. Thank you very much. All right. So again, I think if you look at it, open source, open data, open data is gonna be a new big trend that we're gonna see more and more of.